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Cognitive Computing and Artificial Intelligence Systems in Healthcare

Cognitive Computing and Artificial Intelligence Systems in Healthcare

  • December 2015
  • 69 pages
  • ID: 3471855
  • Format: PDF
  • By Frost & Sullivan

Summary

Table of Contents

Shifts to how healthcare is delivered, where it is delivered, and how it is paid for are necessitating adoption of innovative tools for managing information. A key challenge is to appropriately assess the importance of various quantitative metrics, as well as correlate with qualitative recommendations and patient history. The voluminous forms of unstructured data generated across a wide number of fragmented systems necessitate artificial intelligence-enabled systems to correlate information, recognize patterns, and generate insights. This study looks at the market opportunity for artificial intelligence in healthcare, provides market forecasts, and assesses the competitive landscape of developers.

Key Questions this Study will Answer
What are the examples for cognitive expert systems being used in the healthcare industry?
What are the kinds of business models that would emerge due to the integration of expert cognitive systems within the healthcare space?
What are the market projections for the AI-based services market for healthcare applications?
What are the future trends and applications of these systems in the healthcare industry?
What are the problems with regard to care delivery and how is AI helping overcome them?

Themes for Artificial Intelligence Applications in Healthcare
Partnerships between varied AI technology vendors/companies and hospitals/care providers are of prime importance for wide-scale implementation and subsequent widespread adoption and viability of AI service solutions to answer critical care delivery and hospital information workflow problems.
Tackling machine readability problem from unstructured data (such as blogs, photos, reviews, social media, and data from mobile apps and devices) and gaining further actionable insights from such data remains a critical unmet need among cognitive solution vendors and hospitals alike, which is expected to be handled by new, more powerful systems.
AI is consistently improving the approach and access to reliable and accurate medical image analysis, with help from digital image processing, combined with pattern recognition and machine learning AI platforms. For example, a start-up, Butterfly Network, has developed a handheld 3D-ultrasound tool that creates 3D images of the medical image in real time and sends the data to a cloud service that identifies the characteristics and automates diagnosis. Such clinical support from AI is expected to have a significant impact on the overall medical imaging diagnosis market and its growth.
Innovative automated patient guidance and engagement solutions, such as AI-enabled medication adherence to observe patient adherence (using advanced facial recognition and motion-sensing software), have started to automate one of the major healthcare processes of directly observed therapy (DOT). New entrants with similar solutions are expected to rapidly capture this sub-segment of the market.
Maintaining a smooth patient flow and nurse staffing in hospitals has been a cumbersome challenge for hospitals worldwide. However, real-time monitoring of the hospital environment through the powerful reasoning of AI-enabled solutions has worked wonders for hospital workflow concerns and is expected to have recognizable impact on healthcare delivery.
The AI market for healthcare applications is expected to achieve rapid adoption globally, with a CAGR of xx% until 2021. Excellent patient outcomes, reduced treatment costs, and elimination of unnecessary hospital procedures with easier hospital workflows and patient-centric treatment plans are the prime reasons for the wide adoption and successive growth of the AI market in the healthcare industry.
AI-enabled solutions are expected to be a prominent precursor for the democratization of healthcare (which will have a noticeable global impact), as advances in analytics and AI platforms will push healthcare organizations to perform evidence-driven care (and, in turn, earn bonuses and avoid penalties based on value performance metrics from the government), leading to a more patient-focused, cheap, and value-based care.
Wearable devices, such as wristbands, fitness devices, and smartwatches, are going to augment the AI healthcare applications market by modernizing care through AI algorithms that connect and transmit real-time physiological data from the patient’s wearable device to his/her family physician or hospital database, resulting in proactive monitoring of the patient’s vital statistics and, thus, helping in real-time critical care delivery.

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